UKISUG2014 Big Data Presentation

Data & Analytics

timo-elliott
  • 1. The Big Trends in Big DataTimo Elliott, Global Innovation Evangelist, SAP @timoelliott
  • 2. AgendaBig Data DirectionsUsing Big Data to Improve The Customer ExperienceUsing Big Data to Empower EmployeesUsing Big Data to Optimize Resource UseUsing Big Data for Business NetworksWrap-up© 2014 SAP SE or an SAP affiliate company. All rights reserved. 1
  • 3. Big Data Directions© 2014 SAP SE or an SAP affiliate company. All rights reserved. 2
  • 4. The World Has Turned Upside-DownTransient, flexiblePermanent, fixedANALYTICSOPERATIONS© 2014 SAP SE or an SAP affiliate company. All rights reserved. 4
  • 5. What Is Big Data? The Google Summary …© 2014 SAP SE or an SAP affiliate company. All rights reserved. 7
  • 6. Big Data Is Not Only About “Big” Data“My analytics are becoming more difficult because of the variety and types ofdata sources (not just the volume)”Source: Paradigm4 data scientist survey 2014www.paradigm4.com/wp-content/uploads/2014/06/P4-data-scientist-survey-FINAL.pdf© 2014 SAP SE or an SAP affiliate company. All rights reserved. 8
  • 7. Process dataHuman dataMachine dataBig Data Adds New Data Opportunities© 2014 SAP SE or an SAP affiliate company. All rights reserved. 9
  • 8. Big Data is “Signal” Data© 2014 SAP SE or an SAP affiliate company. All rights reserved. 10
  • 9. Predictive Reaches MaturityDescriptive:What happened?Predictive:What will happen?Diagnostic:Why did it happen?Prescriptive:How can wemake it happen?Hindsight Insight Foresight© 2014 SAP SE or an SAP affiliate company. All rights reserved. 11
  • 10. Companies Don’t Use Most of Their Data TodaySMBs: LEs:Unstructured9 TB 75 TB50TBSemi-structured0.6 TB 5 TB2 TBStructured4 TB 50 TB12 TBOnly12%used todayAverage data volumeper companySource: Forrsights Strategy Spotlight: Business Intelligence And Big Data, Q4 2012. Base: 634 business intelligence users and planners© 2014 SAP SE or an SAP affiliate company. All rights reserved. 12
  • 11. Transactions Are Still a Big Part of Big Data“Which types of data do you anticipate using in the next year?”Source: Paradigm4 data scientist survey 2014www.paradigm4.com/wp-content/uploads/2014/06/P4-data-scientist-survey-FINAL.pdf© 2014 SAP SE or an SAP affiliate company. All rights reserved. 13
  • 12. Big Data Is Heading for the “Trough of Disillusionment”Source: Gartner, August 2014, www.gartner.com/newsroom/id/2819918© 2014 SAP SE or an SAP affiliate company. All rights reserved. 14
  • 13. Benefits from Big Data Initiatives# 5 Identified new product opportunities (6%)#4 More reliable decision making (9%)#3 Improved operational efficiency (11%)#2 Identified new business opportunities (31%)#1 “DON’T KNOW” (51%)Source: Information Difference Research Study Dec 2013: “Big Data Revealed” http://helpit.com/us/industry_articles/big_data_revealed.pdf© 2014 SAP SE or an SAP affiliate company. All rights reserved. 15
  • 14. Hadoop and Other “NoSQL” TechnologyEnterprise “Data Lakes” and “Data Hubs”© 2014 SAP SE or an SAP affiliate company. All rights reserved. 16
  • 15. Hadoop is Complementary, Not a ReplacementSource: Gartner© 2014 SAP SE or an SAP affiliate company. All rights reserved. 17
  • 16. A Typical Example of DW and Hadoop Integration© 2014 SAP SE or an SAP affiliate company. All rights reserved. 18
  • 17. OLTP + OLAP = HTAPHTAP = Hybrid transaction/analytical processingA single system for both OLTP (operational) andOLAP (analytical) processing. Data is stored once, in-memory,and so instantly available for analytics.“Hybrid transaction/analytical processing willempower application leaders to innovate via greatersituation awareness and improved business agility.This will entail an upheaval in the establishedarchitectures, technologies and skills driven by useof in-memory computing technologies as enablers.”Gartner, 2014Source: Gartner 2014, “Hybrid Transaction/Analytical Processing Will Foster Opportunities for DramaticBusiness Innovation”© 2014 SAP SE or an SAP affiliate company. All rights reserved. 19
  • 18. With HTAP, the Operational Schema Looks Like a DWSAP HANASAP HANALive(VirtualData Model)CustomerServiceRisk ManagementTeamFinance andOperationsAccountAdministrationExecutiveManagementCustomers Inventory Channel Products Suppliers Pricing Accounting Planning Forecasting© 2014 SAP SE or an SAP affiliate company. All rights reserved. 20
  • 19. Big Data Architecture Directions: Short TermDataWarehouseBIToolsHadoop HTAPWhere does data arrive?When does it need to move?Where does modeling happen?What can users do themselves?What governance is required?© 2014 SAP SE or an SAP affiliate company. All rights reserved. 21
  • 20. Big Data Architecture Directions: Long TermMetadata abstractionIncreasingly automatedLearning algorithmsContent & PrDoacteas s IncludedMetadata abstractionIncreasingly automatedLearning algorithmsContent and Process IncludedWarehouseBIToolsWhere does data arrive?When does it need to move?Where does modeling happen?What can users do themselves?What governance is required?Integrated Data “SysteHma”d (ocolopud and on-premise) Hadoop HTAPWhere does data arrive?When does it need to move?Where does modeling happen?What can users do themselves?What governance is required?Integrated Data “System” (cloud & on-premise)BITools© 2014 SAP SE or an SAP affiliate company. All rights reserved. 22
  • 21. Opportunity Areas for InnovationBig Data initiatives are typically in one of the following areas:Hyper-personalizeCustomer ExperiencePlan & optimizeResources inReal TimeEngage & empowerWorkforce of theFutureHarness the intelligence ofNetworked Economy© 2014 SAP SE or an SAP affiliate company. All rights reserved. 23
  • 22. Using Big Data to Improve the Customer Experience© 2014 SAP SE or an SAP affiliate company. All rights reserved. 24
  • 23. 80% of CEOs think they deliver a superior customerexperience– but only 8% of customers agree.Source: The New Yorker© 2014 SAP SE or an SAP affiliate company. All rights reserved. 25
  • 24. Personalized Service© 2014 SAP SE or an SAP affiliate company. All rights reserved. 26
  • 25. 27Simplifying SystemsThe benefits of theSAP HANA platformare significant with ahugely simplifiedfootprint.We’re putting thewhole business onthe SAP HANAEnterprise cloud”“
  • 26. Real-Time Retail Insights© 2014 SAP SE or an SAP affiliate company. All rights reserved. 28
  • 27. Social Data© 2014 SAP SE or an SAP affiliate company. All rights reserved. 29
  • 28. Unstructured Data“The improved information flow allows Medtronic to address product performance issuesefficiently, accurately, and effectively and to detect trends at an earlier stage.”© 2014 SAP SE or an SAP affiliate company. All rights reserved. 30
  • 29. New Products and Services© 2014 SAP SE or an SAP affiliate company. All rights reserved. 31
  • 30. Network AnalysisChurn model accuracyimproved by 47% withsocial© 2014 SAP SE or an SAP affiliate company. All rights reserved. 32
  • 31. Sharing Data with Customers© 2014 SAP SE or an SAP affiliate company. All rights reserved. 33
  • 32. © 2014 SAP SE or an SAP affiliate company. All rights reserved. 34
  • 33. Using Big Data to Empower Employees© 2014 SAP SE or an SAP affiliate company. All rights reserved. 35
  • 34. Worldwide, Only 13% of Employees Are Engaged at Work18%52%30%26%57%14%70%17% 16%26%65%9%100%75%50%25%0%USA UK Canada FranceActively DisengagedNot EngagedEngagedSource:Gallup State of the GlobalWorkplace Report 2013© 2014 SAP SE or an SAP affiliate company. All rights reserved. 36
  • 35. Empowering Individual PerformanceAdapting to the analyticsneeds of your employees© 2014 SAP SE or an SAP affiliate company. All rights reserved. 37
  • 36. “Self-Service” Analytics© 2014 SAP SE or an SAP affiliate company. All rights reserved. 38
  • 37. Analytics Collaboration© 2014 SAP SE or an SAP affiliate company. All rights reserved. 39
  • 38. Collaborative Analytics© 2014 SAP SE or an SAP affiliate company. All rights reserved. 40
  • 39. Using Big Data to Optimize Resource Use01011011000101010101010010101001111010101010010111010101010101010010010100100100101110110101010© 2014 SAP SE or an SAP affiliate company. All rights reserved. 41
  • 40. Unilever“if we knew then what we know now, we would have started deployingSAP HANA much earlier, because it’s so important for business... Wethink it’s even more disruptive than we initially thought — we’ve onlyjust started”Marc Béchet, VP Global IT ERP, Unilever© 2014 SAP SE or an SAP affiliate company. All rights reserved. 42
  • 41. Nope© 2014 SAP SE or an SAP affiliate company. All rights reserved. 43
  • 42. Textile Rubber & Chemical Company500 Employees, 4 internal IT staffBusiness Suite on HANAWhy in-memory?Because itsimplified our ITLandscapeIn 5 minutes wecould see moreinformation thanwe could in thelast 7 months”“© 2014 SAP SE or an SAP affiliate company. All rights reserved. 44
  • 43. Big Data Process Mining© 2014 SAP SE or an SAP affiliate company. All rights reserved. 46
  • 44. Wearable devices have grown by 2x month over monthsince October 2012.Source: Mary Meeker’s Internet Trends, 2013Photo: Intel Free Press
  • 45. The “Datafication” of Daily Life© 2014 SAP SE or an SAP affiliate company. All rights reserved. 48
  • 46. Unexpected Uses of Existing DataSource: https://jawbone.com/blog/napa-earthquake-effect-on-sleep/© 2014 SAP SE or an SAP affiliate company. All rights reserved. 49
  • 47. Data, Data, Everywhere© 2014 SAP SE or an SAP affiliate company. All rights reserved. 50
  • 48. Sensors Allow Tracking of the Previously Untrackable© 2014 SAP SE or an SAP affiliate company. All rights reserved. 51
  • 49. Sensors + Cloud + Mobile + Analytics1. Install flow sensors on your beer lines2. The sensors beam data to boxplugged into the internet3. Data sent to HANA inthe cloud4. Mobile interfaces toanalyze consumptionhttp://weissbeerger.com/© 2014 SAP SE or an SAP affiliate company. All rights reserved. 52
  • 50. Sensors + Cloud + Mobile + Analytics (cont.)© 2014 SAP SE or an SAP affiliate company. All rights reserved. 53
  • 51. Networked Crane Safety© 2014 SAP SE or an SAP affiliate company. All rights reserved. 54
  • 52. Sensors + Analytics + Predictive Maintenance© 2014 SAP SE or an SAP affiliate company. All rights reserved. 56
  • 53. Making It Easier to Add Sensors© 2014 SAP SE or an SAP affiliate company. All rights reserved. 57
  • 54. Using Big Data for Business Networks© 2014 SAP SE or an SAP affiliate company. All rights reserved. 58
  • 55. Networked economy: the next economic revolution$0.36T1850Industrialeconomy$12.10T1970$27.50T$90.0TAll figures are in Trillions; 1990 international dollars; Source: Department of Economics, UC Berkeley, BAIN 8 MacroTrends Brief.© 2014 SAP AG or an SAP affiliate company. All rights reserved.ITeconomy1990Interneteconomy2020NetworkedeconomyGrossworldproduct
  • 56. Information Ecosystems60© 2014 SAP SE or an SAP affiliate company. All rights reserved. 60
  • 57. Business Networks Are Becoming Information NetworksProcurementSalesFinanceLogisticsSupply ChainSustainabilityComplianceBuyers SuppliersPartnersAriba NetworkMore than 1M suppliers inmore than 190 countriesaround the worldTransact with suppliers – TheNetwork handles over $460billion per year in commerceReduce supply costs –Customers save a combinedtotal of $82M daily© 2014 SAP SE or an SAP affiliate company. All rights reserved. 61
  • 58. The SAP Big Data Strategy© 2014 SAP SE or an SAP affiliate company. All rights reserved. 62
  • 59. SAP Big Data ArchitectureBig DataDevelopmentToolsIndustryAppsLine ofBusinessAppsDataConnectorsIn-memory &petabyte-scaleETLVisualization& ExplorationAdvancedAnalyticsReportingStreamingAnalyticsBI &© 2014 SAP SE or an SAP affiliate company. All rights reserved. 63
  • 60. Three Core Areas of Big Data StrategyData ScienceBig DataAnalytics & AppsApply AchieveBig Data PlatformAccelerate© 2014 SAP SE or an SAP affiliate company. All rights reserved. 64
  • 61. The SAP HANA Platform and HadoopCustom Apps Mobile Apps Big DataData Ingestion AcquisitionAppsERP Apps SAP AnalyticsSAP HANA PLATFORMIn-memory processing platform for real-time transactions + end-to-endanalytics that offers massive simplification.Extended Application ServicesProcessing EngineDatabase Services(OLTP + OLAP)UnifiedAdministrationApplicationDevelopmentApplication Function Libraries & Data ModelsIntegration ServicesSmart DataAccessTransferDatasetsSAP ERPBWSAP IQWeb /SensorCallCenterOtherData SourcesSAP SLT /Rep ServerSAP SQLAnywhereSAP ESPSAP DataServicesHadoopAdapterHadoopHiveHortonworks DataPlatformIntel Distributionfor HadoopPartner HadoopDistributions© 2014 SAP SE or an SAP affiliate company. All rights reserved. 65
  • 62. Front-End Tools Adapted to Different NeedsDECISIONMAKERDESIGNERExplore MonitorDesignPlan PeopleGovern DATA Enrich ExplainDATAANALYST/SCIENTISTENGAGEEnterprise BIVISUALIZEAgile VisualizationsPREDICTAdvanced Analytics© 2014 SAP SE or an SAP affiliate company. All rights reserved. 66
  • 63. Big Data Applications — E.g., Risk, Sensing, …© 2014 SAP SE or an SAP affiliate company. All rights reserved. 67
  • 64. Design Thinking© 2014 SAP SE or an SAP affiliate company. All rights reserved. 68
  • 65. Wrap-Up© 2014 SAP SE or an SAP affiliate company. All rights reserved. 69
  • 66. 7 Key Points to Take Home1. Big Data is a huge opportunity2. Get closer to your customers through better insight and hyper-personalization3. Use “datafication” to make better use of resources4. Empower your employees to make better decisions5. Leverage your business networks6. Big data is the heart of your next IT platform — simplicity and flexibilityare essential7. The biggest barriers are ideas and culture — use design thinking to help© 2014 SAP SE or an SAP affiliate company. All rights reserved. 70
  • 67. Thank youTimo Elliott, SAPtimo.elliott@sap.comTwitter: @timoelliottBlog: timoelliott.com© 2014 SAP SE or an SAP affiliate company. All rights reserved.
  • 68. © 2014 SAP SE or an SAP affiliate company.All rights reserved.No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of SAP SE or anSAP affiliate company.SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of SAP SE(or an SAP affiliate company) in Germany and other countries. Please see http://global12.sap.com/corporate-en/legal/copyright/index.epx for additionaltrademark information and notices.Some software products marketed by SAP SE and its distributors contain proprietary software components of other software vendors.National product specifications may vary.These materials are provided by SAP SE or an SAP affiliate company for informational purposes only, without representation or warranty of any kind,and SAP SE or its affiliated companies shall not be liable for errors or omissions with respect to the materials. The only warranties for SAP SE orSAP affiliate company products and services are those that are set forth in the express warranty statements accompanying such products andservices, if any. Nothing herein should be construed as constituting an additional warranty.In particular, SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or any relatedpresentation, or to develop or release any functionality mentioned therein. This document, or any related presentation, and SAP SE’s or its affiliatedcompanies’ strategy and possible future developments, products, and/or platform directions and functionality are all subject to change and may bechanged by SAP SE or its affiliated companies at any time for any reason without notice. The information in this document is not a commitment,promise, or legal obligation to deliver any material, code, or functionality. All forward-looking statements are subject to various risks and uncertaintiesthat could cause actual results to differ materially from expectations. Readers are cautioned not to place undue reliance on these forward-lookingstatements, which speak only as of their dates, and they should not be relied upon in making purchasing decisions.© 2014 SAP SE or an SAP affiliate company. All rights reserved. 72
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    Big Data presentation at UK & Ireland SAP User Group, in Birmingham UK, November 2014
    Text
    • 1. The Big Trends in Big DataTimo Elliott, Global Innovation Evangelist, SAP @timoelliott
  • 2. AgendaBig Data DirectionsUsing Big Data to Improve The Customer ExperienceUsing Big Data to Empower EmployeesUsing Big Data to Optimize Resource UseUsing Big Data for Business NetworksWrap-up© 2014 SAP SE or an SAP affiliate company. All rights reserved. 1
  • 3. Big Data Directions© 2014 SAP SE or an SAP affiliate company. All rights reserved. 2
  • 4. The World Has Turned Upside-DownTransient, flexiblePermanent, fixedANALYTICSOPERATIONS© 2014 SAP SE or an SAP affiliate company. All rights reserved. 4
  • 5. What Is Big Data? The Google Summary …© 2014 SAP SE or an SAP affiliate company. All rights reserved. 7
  • 6. Big Data Is Not Only About “Big” Data“My analytics are becoming more difficult because of the variety and types ofdata sources (not just the volume)”Source: Paradigm4 data scientist survey 2014www.paradigm4.com/wp-content/uploads/2014/06/P4-data-scientist-survey-FINAL.pdf© 2014 SAP SE or an SAP affiliate company. All rights reserved. 8
  • 7. Process dataHuman dataMachine dataBig Data Adds New Data Opportunities© 2014 SAP SE or an SAP affiliate company. All rights reserved. 9
  • 8. Big Data is “Signal” Data© 2014 SAP SE or an SAP affiliate company. All rights reserved. 10
  • 9. Predictive Reaches MaturityDescriptive:What happened?Predictive:What will happen?Diagnostic:Why did it happen?Prescriptive:How can wemake it happen?Hindsight Insight Foresight© 2014 SAP SE or an SAP affiliate company. All rights reserved. 11
  • 10. Companies Don’t Use Most of Their Data TodaySMBs: LEs:Unstructured9 TB 75 TB50TBSemi-structured0.6 TB 5 TB2 TBStructured4 TB 50 TB12 TBOnly12%used todayAverage data volumeper companySource: Forrsights Strategy Spotlight: Business Intelligence And Big Data, Q4 2012. Base: 634 business intelligence users and planners© 2014 SAP SE or an SAP affiliate company. All rights reserved. 12
  • 11. Transactions Are Still a Big Part of Big Data“Which types of data do you anticipate using in the next year?”Source: Paradigm4 data scientist survey 2014www.paradigm4.com/wp-content/uploads/2014/06/P4-data-scientist-survey-FINAL.pdf© 2014 SAP SE or an SAP affiliate company. All rights reserved. 13
  • 12. Big Data Is Heading for the “Trough of Disillusionment”Source: Gartner, August 2014, www.gartner.com/newsroom/id/2819918© 2014 SAP SE or an SAP affiliate company. All rights reserved. 14
  • 13. Benefits from Big Data Initiatives# 5 Identified new product opportunities (6%)#4 More reliable decision making (9%)#3 Improved operational efficiency (11%)#2 Identified new business opportunities (31%)#1 “DON’T KNOW” (51%)Source: Information Difference Research Study Dec 2013: “Big Data Revealed” http://helpit.com/us/industry_articles/big_data_revealed.pdf© 2014 SAP SE or an SAP affiliate company. All rights reserved. 15
  • 14. Hadoop and Other “NoSQL” TechnologyEnterprise “Data Lakes” and “Data Hubs”© 2014 SAP SE or an SAP affiliate company. All rights reserved. 16
  • 15. Hadoop is Complementary, Not a ReplacementSource: Gartner© 2014 SAP SE or an SAP affiliate company. All rights reserved. 17
  • 16. A Typical Example of DW and Hadoop Integration© 2014 SAP SE or an SAP affiliate company. All rights reserved. 18
  • 17. OLTP + OLAP = HTAPHTAP = Hybrid transaction/analytical processingA single system for both OLTP (operational) andOLAP (analytical) processing. Data is stored once, in-memory,and so instantly available for analytics.“Hybrid transaction/analytical processing willempower application leaders to innovate via greatersituation awareness and improved business agility.This will entail an upheaval in the establishedarchitectures, technologies and skills driven by useof in-memory computing technologies as enablers.”Gartner, 2014Source: Gartner 2014, “Hybrid Transaction/Analytical Processing Will Foster Opportunities for DramaticBusiness Innovation”© 2014 SAP SE or an SAP affiliate company. All rights reserved. 19
  • 18. With HTAP, the Operational Schema Looks Like a DWSAP HANASAP HANALive(VirtualData Model)CustomerServiceRisk ManagementTeamFinance andOperationsAccountAdministrationExecutiveManagementCustomers Inventory Channel Products Suppliers Pricing Accounting Planning Forecasting© 2014 SAP SE or an SAP affiliate company. All rights reserved. 20
  • 19. Big Data Architecture Directions: Short TermDataWarehouseBIToolsHadoop HTAPWhere does data arrive?When does it need to move?Where does modeling happen?What can users do themselves?What governance is required?© 2014 SAP SE or an SAP affiliate company. All rights reserved. 21
  • 20. Big Data Architecture Directions: Long TermMetadata abstractionIncreasingly automatedLearning algorithmsContent & PrDoacteas s IncludedMetadata abstractionIncreasingly automatedLearning algorithmsContent and Process IncludedWarehouseBIToolsWhere does data arrive?When does it need to move?Where does modeling happen?What can users do themselves?What governance is required?Integrated Data “SysteHma”d (ocolopud and on-premise) Hadoop HTAPWhere does data arrive?When does it need to move?Where does modeling happen?What can users do themselves?What governance is required?Integrated Data “System” (cloud & on-premise)BITools© 2014 SAP SE or an SAP affiliate company. All rights reserved. 22
  • 21. Opportunity Areas for InnovationBig Data initiatives are typically in one of the following areas:Hyper-personalizeCustomer ExperiencePlan & optimizeResources inReal TimeEngage & empowerWorkforce of theFutureHarness the intelligence ofNetworked Economy© 2014 SAP SE or an SAP affiliate company. All rights reserved. 23
  • 22. Using Big Data to Improve the Customer Experience© 2014 SAP SE or an SAP affiliate company. All rights reserved. 24
  • 23. 80% of CEOs think they deliver a superior customerexperience– but only 8% of customers agree.Source: The New Yorker© 2014 SAP SE or an SAP affiliate company. All rights reserved. 25
  • 24. Personalized Service© 2014 SAP SE or an SAP affiliate company. All rights reserved. 26
  • 25. 27Simplifying SystemsThe benefits of theSAP HANA platformare significant with ahugely simplifiedfootprint.We’re putting thewhole business onthe SAP HANAEnterprise cloud”“
  • 26. Real-Time Retail Insights© 2014 SAP SE or an SAP affiliate company. All rights reserved. 28
  • 27. Social Data© 2014 SAP SE or an SAP affiliate company. All rights reserved. 29
  • 28. Unstructured Data“The improved information flow allows Medtronic to address product performance issuesefficiently, accurately, and effectively and to detect trends at an earlier stage.”© 2014 SAP SE or an SAP affiliate company. All rights reserved. 30
  • 29. New Products and Services© 2014 SAP SE or an SAP affiliate company. All rights reserved. 31
  • 30. Network AnalysisChurn model accuracyimproved by 47% withsocial© 2014 SAP SE or an SAP affiliate company. All rights reserved. 32
  • 31. Sharing Data with Customers© 2014 SAP SE or an SAP affiliate company. All rights reserved. 33
  • 32. © 2014 SAP SE or an SAP affiliate company. All rights reserved. 34
  • 33. Using Big Data to Empower Employees© 2014 SAP SE or an SAP affiliate company. All rights reserved. 35
  • 34. Worldwide, Only 13% of Employees Are Engaged at Work18%52%30%26%57%14%70%17% 16%26%65%9%100%75%50%25%0%USA UK Canada FranceActively DisengagedNot EngagedEngagedSource:Gallup State of the GlobalWorkplace Report 2013© 2014 SAP SE or an SAP affiliate company. All rights reserved. 36
  • 35. Empowering Individual PerformanceAdapting to the analyticsneeds of your employees© 2014 SAP SE or an SAP affiliate company. All rights reserved. 37
  • 36. “Self-Service” Analytics© 2014 SAP SE or an SAP affiliate company. All rights reserved. 38
  • 37. Analytics Collaboration© 2014 SAP SE or an SAP affiliate company. All rights reserved. 39
  • 38. Collaborative Analytics© 2014 SAP SE or an SAP affiliate company. All rights reserved. 40
  • 39. Using Big Data to Optimize Resource Use01011011000101010101010010101001111010101010010111010101010101010010010100100100101110110101010© 2014 SAP SE or an SAP affiliate company. All rights reserved. 41
  • 40. Unilever“if we knew then what we know now, we would have started deployingSAP HANA much earlier, because it’s so important for business... Wethink it’s even more disruptive than we initially thought — we’ve onlyjust started”Marc Béchet, VP Global IT ERP, Unilever© 2014 SAP SE or an SAP affiliate company. All rights reserved. 42
  • 41. Nope© 2014 SAP SE or an SAP affiliate company. All rights reserved. 43
  • 42. Textile Rubber & Chemical Company500 Employees, 4 internal IT staffBusiness Suite on HANAWhy in-memory?Because itsimplified our ITLandscapeIn 5 minutes wecould see moreinformation thanwe could in thelast 7 months”“© 2014 SAP SE or an SAP affiliate company. All rights reserved. 44
  • 43. Big Data Process Mining© 2014 SAP SE or an SAP affiliate company. All rights reserved. 46
  • 44. Wearable devices have grown by 2x month over monthsince October 2012.Source: Mary Meeker’s Internet Trends, 2013Photo: Intel Free Press
  • 45. The “Datafication” of Daily Life© 2014 SAP SE or an SAP affiliate company. All rights reserved. 48
  • 46. Unexpected Uses of Existing DataSource: https://jawbone.com/blog/napa-earthquake-effect-on-sleep/© 2014 SAP SE or an SAP affiliate company. All rights reserved. 49
  • 47. Data, Data, Everywhere© 2014 SAP SE or an SAP affiliate company. All rights reserved. 50
  • 48. Sensors Allow Tracking of the Previously Untrackable© 2014 SAP SE or an SAP affiliate company. All rights reserved. 51
  • 49. Sensors + Cloud + Mobile + Analytics1. Install flow sensors on your beer lines2. The sensors beam data to boxplugged into the internet3. Data sent to HANA inthe cloud4. Mobile interfaces toanalyze consumptionhttp://weissbeerger.com/© 2014 SAP SE or an SAP affiliate company. All rights reserved. 52
  • 50. Sensors + Cloud + Mobile + Analytics (cont.)© 2014 SAP SE or an SAP affiliate company. All rights reserved. 53
  • 51. Networked Crane Safety© 2014 SAP SE or an SAP affiliate company. All rights reserved. 54
  • 52. Sensors + Analytics + Predictive Maintenance© 2014 SAP SE or an SAP affiliate company. All rights reserved. 56
  • 53. Making It Easier to Add Sensors© 2014 SAP SE or an SAP affiliate company. All rights reserved. 57
  • 54. Using Big Data for Business Networks© 2014 SAP SE or an SAP affiliate company. All rights reserved. 58
  • 55. Networked economy: the next economic revolution$0.36T1850Industrialeconomy$12.10T1970$27.50T$90.0TAll figures are in Trillions; 1990 international dollars; Source: Department of Economics, UC Berkeley, BAIN 8 MacroTrends Brief.© 2014 SAP AG or an SAP affiliate company. All rights reserved.ITeconomy1990Interneteconomy2020NetworkedeconomyGrossworldproduct
  • 56. Information Ecosystems60© 2014 SAP SE or an SAP affiliate company. All rights reserved. 60
  • 57. Business Networks Are Becoming Information NetworksProcurementSalesFinanceLogisticsSupply ChainSustainabilityComplianceBuyers SuppliersPartnersAriba NetworkMore than 1M suppliers inmore than 190 countriesaround the worldTransact with suppliers – TheNetwork handles over $460billion per year in commerceReduce supply costs –Customers save a combinedtotal of $82M daily© 2014 SAP SE or an SAP affiliate company. All rights reserved. 61
  • 58. The SAP Big Data Strategy© 2014 SAP SE or an SAP affiliate company. All rights reserved. 62
  • 59. SAP Big Data ArchitectureBig DataDevelopmentToolsIndustryAppsLine ofBusinessAppsDataConnectorsIn-memory &petabyte-scaleETLVisualization& ExplorationAdvancedAnalyticsReportingStreamingAnalyticsBI &© 2014 SAP SE or an SAP affiliate company. All rights reserved. 63
  • 60. Three Core Areas of Big Data StrategyData ScienceBig DataAnalytics & AppsApply AchieveBig Data PlatformAccelerate© 2014 SAP SE or an SAP affiliate company. All rights reserved. 64
  • 61. The SAP HANA Platform and HadoopCustom Apps Mobile Apps Big DataData Ingestion AcquisitionAppsERP Apps SAP AnalyticsSAP HANA PLATFORMIn-memory processing platform for real-time transactions + end-to-endanalytics that offers massive simplification.Extended Application ServicesProcessing EngineDatabase Services(OLTP + OLAP)UnifiedAdministrationApplicationDevelopmentApplication Function Libraries & Data ModelsIntegration ServicesSmart DataAccessTransferDatasetsSAP ERPBWSAP IQWeb /SensorCallCenterOtherData SourcesSAP SLT /Rep ServerSAP SQLAnywhereSAP ESPSAP DataServicesHadoopAdapterHadoopHiveHortonworks DataPlatformIntel Distributionfor HadoopPartner HadoopDistributions© 2014 SAP SE or an SAP affiliate company. All rights reserved. 65
  • 62. Front-End Tools Adapted to Different NeedsDECISIONMAKERDESIGNERExplore MonitorDesignPlan PeopleGovern DATA Enrich ExplainDATAANALYST/SCIENTISTENGAGEEnterprise BIVISUALIZEAgile VisualizationsPREDICTAdvanced Analytics© 2014 SAP SE or an SAP affiliate company. All rights reserved. 66
  • 63. Big Data Applications — E.g., Risk, Sensing, …© 2014 SAP SE or an SAP affiliate company. All rights reserved. 67
  • 64. Design Thinking© 2014 SAP SE or an SAP affiliate company. All rights reserved. 68
  • 65. Wrap-Up© 2014 SAP SE or an SAP affiliate company. All rights reserved. 69
  • 66. 7 Key Points to Take Home1. Big Data is a huge opportunity2. Get closer to your customers through better insight and hyper-personalization3. Use “datafication” to make better use of resources4. Empower your employees to make better decisions5. Leverage your business networks6. Big data is the heart of your next IT platform — simplicity and flexibilityare essential7. The biggest barriers are ideas and culture — use design thinking to help© 2014 SAP SE or an SAP affiliate company. All rights reserved. 70
  • 67. Thank youTimo Elliott, SAPtimo.elliott@sap.comTwitter: @timoelliottBlog: timoelliott.com© 2014 SAP SE or an SAP affiliate company. All rights reserved.
  • 68. © 2014 SAP SE or an SAP affiliate company.All rights reserved.No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of SAP SE or anSAP affiliate company.SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of SAP SE(or an SAP affiliate company) in Germany and other countries. Please see http://global12.sap.com/corporate-en/legal/copyright/index.epx for additionaltrademark information and notices.Some software products marketed by SAP SE and its distributors contain proprietary software components of other software vendors.National product specifications may vary.These materials are provided by SAP SE or an SAP affiliate company for informational purposes only, without representation or warranty of any kind,and SAP SE or its affiliated companies shall not be liable for errors or omissions with respect to the materials. The only warranties for SAP SE orSAP affiliate company products and services are those that are set forth in the express warranty statements accompanying such products andservices, if any. Nothing herein should be construed as constituting an additional warranty.In particular, SAP SE or its affiliated companies have no obligation to pursue any course of business outlined in this document or any relatedpresentation, or to develop or release any functionality mentioned therein. This document, or any related presentation, and SAP SE’s or its affiliatedcompanies’ strategy and possible future developments, products, and/or platform directions and functionality are all subject to change and may bechanged by SAP SE or its affiliated companies at any time for any reason without notice. The information in this document is not a commitment,promise, or legal obligation to deliver any material, code, or functionality. All forward-looking statements are subject to various risks and uncertaintiesthat could cause actual results to differ materially from expectations. Readers are cautioned not to place undue reliance on these forward-lookingstatements, which speak only as of their dates, and they should not be relied upon in making purchasing decisions.© 2014 SAP SE or an SAP affiliate company. All rights reserved. 72
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